An efficient approach for texture classification with multi-resolution features by combining region and edge information using a modified CSNN

In this paper, we propose an efficient approach for texture segmentation by integrating region and edge information. The algorithm uses a constraint satisfaction neural network (CSNN) for texture segmentation with additional edge constraints. Initial class probabilities (segmented map) and edge maps are obtained from the image using two stages of multi-channel, multi-resolution filters. The complementary information of the segmented map and the edge map are iteratively updated using a modified CSNN to satisfy a set of constraints to obtain superior segmentation results. The proposed methodology is tested on simulated as well as natural textures and provides satisfactory results.